Statistical analyses
Analyses of the above trait values were performed in four steps with R
3.5.2 (R core team 2018).
First, we tested for divergent trait evolution in plants descending from
dry, control and wet manipulated plots in the central sites SA and M
(N=240 genotypes). For each trait separately, linear mixed models were
calculated with climate manipulation treatment (dry, control, wet), site
(SA, M), five greenhouse watering levels, and their interactions as
fixed factors, as well as genotype as random factor (accounting for five
non-independent plants across water levels). Some traits were
transformed prior to analyses to meet homoscedasticity (sqrt: stomata
density, height, reproductive allocation, seed number; log: leaf number
at flowering, vegetative biomass).
Significance was assessed with
Wald
F-tests with Kenward-Roger approximated df in the package car (Fox &
Weisberg 2011). Posthoc tests identified contrasting climate
manipulations using the package ‘multcomp’ (Hothorn et al. 2008)
with P-values corrected for false discovery rate (FDR) sensuBenjamini & Hochberg (1995). For germination fraction (binary) we used
a corresponding glm with logit link-function and quasibinomial error
structure.
Second, we tested for clinal trends in traits across the rainfall
gradient, including only plants descending from control plots in all
four sites (N=160 genotypes). We calculated linear mixed models per
trait with site and greenhouse water level as fixed factors, and
genotype as random factor (transformations as above). Posthoc tests with
FDR-correction as above identified contrasting sites. Germination
fraction was analyzed with a binomial glm as above, using only site as
main factor.
Third, we estimated selection, i.e. the covariance of traits with
relative fitness (Lande & Arnold 1983), under low and high irrigation
in the greenhouse. This approach reveals traits that can adapt a
population to drought and is independent of other environmental factors
correlating with rainfall (Mitchell-Olds & Schmitt 2006). We estimated
selection for all traits showing either rapid evolution (step 1) or
clines with rainfall (step 2). We included all plants from sites with
climate manipulation, computed the genotype trait-mean across low
watering (15ml, 20ml) and high watering (50ml, 90ml), followed by
standardization (zero mean, 1 SD) per population (SA and M) and watering
level. Similarly, relative fitness was computed per population and
watering. We fitted generalized least squares models (gls, rmspackage (Harrell 2019)), with relative fitness as the dependent
variable, and trait, watering (high, low) and their interaction as
predictors. A significant trait × watering interaction indicated
contrasting directional selection on that trait contingent on water
availability, computed using type III sums of squares (Anova, carpackage (Fox & Weisberg 2019)) with FDR-correction.
Fourth, we tested whether field climate manipulations favored genotypes
with higher plasticity. In addition to assessing the climate
manipulation × water term in step 1 above, plasticity was quantified for
the above traits using the Coefficient of Variation (CV) across the five
individuals (i.e. water levels) per genotype in the greenhouse. The
intuitive, standardized CV allows comparing plasticity across different
traits (Houle 1992) and handles well outliers and non-linear responses
across several environments. Another plasticity index
(PIv, see Valladares et al. 2006) yielded the
same results. With these CV-values per genotype, we calculated two-way
ANOVAs and FDR-post hoc tests separately for each trait, including the
factors site (SA, M) and climate change treatment (dry, control, wet).